Preparations

Load the necessary libraries

library(tidyverse) #for data wrangling

Link to the data transformation cheatsheet

https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf

Important data manipulation functions:

Task	Function	Package
Sorting	`arrange()`	dplyr
Adding columns	`mutate()`	dplyr
Transformations	`mutate()`	dplyr
Re-ordering factor levels	`factor(,levels=)`	base
Re-labelling	`factor(,lab=)`	base
	`recode()`	dplyr
Re-naming columns	`rename(,replace=)`	dplyr
Filtering/Subsetting	indexing	base
~ columns	`select(,...)`	dplyr
	`pull(,...)`	dplyr
~ rows	`filter(,...)`	dplyr
Unique combinations	`distinct()`	dplyr
Reshaping data	`pivot_longer()`, `pivot_wider()`	tidyr
Split/combine columns	`separate()`, `unite()`	tidyr
Aggregating	`group_by()` `summarise()`	dplyr
	`group_by()` `count()`	dplyr
Merging/joining	`*_join()`	dplyr
Extracting data structure	`expand()`	tidyr
	`crossing()`	tidyr

Piping

Data files

load(file='../data/manipulationDatasets.RData')
dat.1 %>% head

Sorting data

head(dat.1)

#OR
dat.1 %>% head()

#OR
dat.1 %>% head

Adding columns - mutate

Summarising (aggregating) data

Grouping (=aggregating)

Subset columns

Regular expressions (regex)

https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf

Filtering

Reshaping data

Pivot longer

Pivot wider

Combining data

Applied examples